Automated network control systems that adapt network configurations based on the local network environment

ABSTRACT

Systems, apparatuses and methods may provide for technology that adjusts, via a short-term subsystem, a communications parameter for one or more of wireless communication devices based on data from one or more of a plurality of sensors. The technology may also determine, via a neural network, a prediction of future performance of the wireless network based on a state of the network environment, wherein the state of the network environment includes information from the short-term subsystem and location information about the wireless communication devices and other objects in the environment, and determine a change in network configuration to improve a quality of communications in the wireless network based on the prediction of future performance of the wireless network. The technology may further generate generic path loss models based on time-stamped RSSI maps and record a sequence of events that cause a significant drop in RSSI to determine a change in network configuration.

TECHNICAL FIELD

Embodiments generally relate to automated control systems. Moreparticularly, embodiments relate to technology for automated networkcontrol systems that adapt a network configuration based on the localnetwork environment.

BACKGROUND

Automated control systems may be used in a variety of environments suchas, for example, industrial environments. In industrial environments,communications between devices are often required to be highly reliable.Increasing the reliability of communications has typically beenassociated with wired interfaces, where the capacity of thecommunication channel is fixed and specific to the physical propertiesof the wired propagation medium. Wired communications networks presentdisadvantages, however, such as cost of deployment and lack ofdeployment flexibility. While highly reliable wireless communications inthese kinds of environments may be desirable, the stochastic nature ofthe wireless propagation medium makes extensive monitoring of thenetwork necessary to guarantee packet delivery rates (PDR) compliantwith network requirements. Industrial environments, where a wirelesschannel typically exhibits long delay spread, provide unique challengesto achieving highly reliable wireless communications. Furthermore,changes in the environment can cause sudden degradation of the wirelesschannel, thus impacting reliability. Reliability can be also affected bythe presence of interfering sources, such as moving objects. Althoughfrequency and spatial redundancy techniques can be used to address theseissues, these techniques have disadvantages, such as the cost ofduplicating information sent.

BRIEF DESCRIPTION OF THE DRAWINGS

The various advantages of the embodiments will become apparent to oneskilled in the art by reading the following specification and appendedclaims, and by referencing the following drawings, in which:

FIGS. 1A-1B provide block diagrams illustrating examples of a wirelessnetwork control system according to one or more embodiments;

FIG. 2 is a diagram illustrating aspects of an example of a wirelessnetwork control system in an industrial environment according to one ormore embodiments;

FIG. 3 is a diagram illustrating aspects of an example of dataenhancement in a wireless network control system according to one ormore embodiments;

FIGS. 4A-4B provide diagrams illustrating aspects of an example oftraining and inference phases for a wireless network control systemaccording to one or more embodiments;

FIG. 5 is a diagram illustrating an example of application of a wirelessnetwork control system in an industrial environment according to one ormore embodiments;

FIGS. 6A-6B provide flowcharts illustrating operation of an example of awireless network control system according to one or more embodiments;

FIG. 7 is a block diagram illustrating an example of aperformance-enhanced computing system according to one or moreembodiments;

FIG. 8 is a block diagram illustrating an example of a semiconductorapparatus according to one or more embodiments;

FIG. 9 is a block diagram illustrating an example of a processoraccording to one or more embodiments; and

FIG. 10 is a block diagram illustrating an example of amultiprocessor-based computing system according to one or moreembodiments.

DESCRIPTION OF EMBODIMENTS

In general, embodiments provide an automated network control system thatadapts a network configuration based on the local network environment,such as, for example, an industrial setting. Embodiments also providefor collecting sensory information and learning wireless propagationparameters of moving objects in the environment. Additionally,embodiments include technology that predicts the effects thatenvironmental changes have on wireless signal quality and adjuststransmission modes accordingly. Embodiments also provide for using alearned channel model to predict the effect of different configurationsin the network traffic quality and jointly optimize configuration forall network devices.

More particularly, embodiments of the wireless network control systemuse a multi-unit structure including a short-term store subsystem thatimplements time-critical responses, and a long-term store subsystem thatcoordinates non-time critical responses for maintaining overall networkreliability. Embodiments, thus, provide technology enabling the networkcontrol system to make immediate changes in communication (e.g. to avoidinterference) and also learn coordinated decisions that improve thequality of communications in the future. The network control system maycollect information about the local environment in which the systemoperates using a myriad of sensory registers such as, e.g., cameras,radar, and RF sniffer nodes. Information collected by these sensor nodesmay be processed and stored in short-term and long-term memory modules.

FIG. 1A is a block diagram illustrating an overview of an examplewireless network control system 100 according to one or moreembodiments, with reference to components and features described hereinincluding but not limited to the figures and associated description. Thewireless network control system 100 may include a set of sensoryregisters (i.e., sensor nodes) 110, short-term subsystem 120, long-termsubsystem 130, and output response module 140. The wireless networkcontrol system 100 may receive as input environmental signals 150, whichmay include signals such as, e.g., RF signals from one or more accesspoints (APs), one or more stations (STAs) and/or from other nodes, andimagery or video from one or more cameras, signals from radar or lidar,etc. The wireless network control system 100 may also receive as inputenvironmental conditions 160, which may include identification andlocation information for objects in the environment, such as, e.g.automated guided vehicles (AGVs) and robotic arms, as well as accesspoints and stations. The environmental signals 150 may be received andprocessed together with environmental conditions 160 via sensoryregisters 110. Network information such as network logs, includingroutes and PDRs, may also be collected or accumulated via sensoryregisters 110.

A short-term subsystem 120 may receive and store information fromsensory registers 110. This information may be used by short-termsubsystem 120 to make uncoordinated local time-critical decisions, andmay be stored for short periods of time. The short-term subsystem 120may also provide information to a long-term subsystem 130 and to anoutput response module 140, where network management actions may bedecided based at least in part on the information given by theshort-term subsystem 120. Operation of the short-term subsystem 120 maybe considered analogous to muscle memory in a biological model. Forinstance, touching a hot surface would produce an immediatetime-critical muscle response to cause movement (e.g. hand or arm) awayfrom the heat source as soon as possible to avoid skin burns.

The long-term subsystem 130 may receive information from sensoryregisters 110 and from the short-term subsystem 120 associated todifferent events. The long-term subsystem 130 may process thisinformation and generate learnings to make coordinated non time-criticaldecisions to ensure overall reliability of the network, which areprovided to output response module 140. Learnings are stored in thelong-term subsystem 130 for accumulation of knowledge over time. Likethe short-term subsystem 120, an analogy may be drawn between thelong-term subsystem 130 and biological system reaction to touching a hotsurface. Information extracted from short-term biological memory, i.e.senses (smell, sight, hearing . . . ), is processed and stored tounderstand the conditions that caused the skin to be burned, and is usedto prevent this event from happening again in the future.

The output response module 140 may receive input from the short-termsubsystem 120 and/or from the long-term subsystem 130, and may generatenetwork configuration (or reconfiguration) commands 170. Networkconfiguration or reconfiguration commands may include, for example:change in transmitting power for an AP, change of STA-AP association forefficient handover, change in Wi-Fi channel due to excessiveinterference, etc.

FIG. 1B is block diagram illustrating an example wireless networkcontrol system 100 according to one or more embodiments, with referenceto components and features described herein including but not limited tothe figures and associated description. FIG. 1B includes the samereference numerals as in FIG. 1A, and includes further detail for theshort-term subsystem 120, the long-term subsystem 130, and the outputresponse module 140. Consistent with the system components as describedwith reference to FIG. 1A, the short-term subsystem 120 may makeuncoordinated, low latency, local decisions, while the long-termsubsystem 130 may use information extracted from short-term devices todetect behavioral patterns and make coordinated non time-criticaldecisions. Sensory registers 110 may provide data to the short-termsubsystem 120 and the long-term subsystem 130 via an interface 115.

The short-term subsystem 120 may include a set of devices (1 . . . N)and a set of short term memory modules 122 ₁ . . . 122 _(N) which arephysically distributed among and associated with the devices (1 . . .N), such as automated guided vehicles (AGVs), access points (APs), etc.Some devices (e.g., access points) may be in a fixed location, whileother devices (e.g., AGVs) may be mobile. Thus, these short-term memorymodules 122 ₁ . . . 122 _(N) may not be in the same physical locationacross the environment. Any particular short-term memory module 122, (ofthe set of modules 122 ₁ . . . 122 _(N)) does not have observability ofother short-term modules in the system. The short-term subsystem 120 mayoperate via a set of algorithms processing different sensory registersand generating new learnings/memories. Such algorithms may include, forexample, pattern recognition for local radio (for local radiomanagement), image processing (using on board camera for collisionavoidance), localization (indoor positioning), etc. Each short-termmemory module 122, (of the set of modules 122 ₁ . . . 122 _(N)) mayprovide an output response i to output response module 140, which mayalso be distributed with the devices as outputs 142 ₁ . . . 142 _(N)across the physical environment. An output response based on short-termlearnings is generated only when the action required is time critical(i.e., the action cannot await coordinated response or action by thecentral controller) and specific to the device (i.e. information fromother devices is not needed to determine the action to be taken). Forexample, for an autonomous mobile device operating in an indoorindustrial environment, a short-term memory module may take care ofcollision avoidance-related actions. In addition, a short-term memorymodule may be used to obtain an instantaneous location estimation of theautonomous mobile device. Learnings from the short-term subsystem 120are discarded after an output response is generated. Before short-termmemories are discarded, however, they are shared with the long-termsubsystem 130 for further processing.

The long-term subsystem 130 may include a neural network, which may be arecurrent neural network such as a long short-term memory (LSTM) neuralnetwork. The neural network may be implemented in a field programmablegate array (FPGA) accelerator; in some embodiments, the neural networkmay be implemented in a combination of a processor and a FPGAaccelerator. The long-term subsystem 130 may collect information fromeach short-term memory module 122 (of the set of modules 122 ₁ . . . 122_(N)) and process it to identify patterns (pattern recognition element132, which may be performed via the neural network) that have an impacton the overall performance of the communications network. To estimatenetwork performance from a sequence of short-term events (from theshort-term subsystem 120), the long-term subsystem 130 may continuouslyupdate its knowledge of the wireless environment. Unlike short-termsubsystem 120, the long-term subsystem 130 may be placed in a singlephysical location (e.g., in an edge server or in a central networkcontroller), and the learnings extracted from analyzing the data flowsfrom different short-term modules may be stored permanently (long-termmemory 134). The long-term subsystem 130 may use the learnings todetermine a configuration (or reconfiguration) of the communicationsnetwork to improve or optimize network performance (networkconfiguration element 136, which may be performed by a processor orlogic other than the neural network), which is then provided to theoutput response module 140 to implement the new network configuration(or reconfiguration). As one example, the long-term subsystem 130 mayreconfigure the associations between STAs and APs to enable efficientSTA-AP handover.

FIG. 2 is a diagram illustrating aspects of an example of a wirelessnetwork control system 200 in an industrial environment according to oneor more embodiments, with reference to components and features describedherein including but not limited to the figures and associateddescription. In the scenario illustrated in FIG. 2 , an industrialwireless network may be formed by a central network controller (CNC)210, multiple access points (AP), multiple stations (STAs) 220 (labeledSTA1 through STA5), and multiple surveillance cameras 230; in someinstances, cameras may be bundled with access points and sensors to formAP-cam units 240. The CNC 210 may coordinate actions and communicationsoccurring in the whole network. The access points 240 may be connectedto the CNC 210 and provide wireless connectivity to all stations 220 inthe environment. The stations 220 may be located in moving objects suchas automated guided vehicles (AGVs) or in static objects such as roboticarms. Reliable communications between access points 240 and stations 220are necessary to service the AGVs and robotic arms. The cameras 230 andthe AP-cam units 240, as deployed throughout the environment, maymonitor the environment to track objects and assets. In someembodiments, APs may be separate from cameras.

According to embodiments, the wireless network control system 200 mayhave a hierarchical memory system including a short-term subsystem 120(FIGS. 1A-1B), already discussed, and a long-term subsystem 130 (FIGS.1A-1B), already discussed. In the industrial example of FIG. 2 , theshort-term subsystem 120 may have short-term memory formed by learningsextracted from sensor data processed by access points 240 and stations230, and the long-term subsystem 130 may be incorporated into the CNC210. For example, stations configured in sniffer mode may listen to eachother while they are not scheduled for any transmission or datareception. In this case, the short-term learnings may consist oftime-stamped received signal strength indication (RSSI) maps generatedfor each of stations and stored in the access points. All STAs connectedto an AP may share their RSSI along with their location coordinates.This information may be used to generate a RSSI map. The CNC 210 maycollect this information together with object tracking information fromcameras 230 (and AP-cams 240), and may determine relationships betweenRSSI values and object locations relative to the transmitter stations(APs) and receiver stations (STAs). For example, by overhearing otherSTA transmissions, interference from STAs operating in the same channelmay be predicted. This may be used to provide an optimal powerallocation and wireless communication scheduling policy that minimizesco-channel interference. Additionally, based on the RSSI maps, efficientSTA-AP handover may be devised for high reliable wirelesscommunications.

FIG. 3 is a diagram 300 illustrating an example of object classificationfeature enrichment in wireless network control system according to oneor more embodiments, with reference to components and features describedherein including but not limited to the figures and associateddescription. As shown in FIG. 3 , a central network controller (CNC)having a long-term subsystem may collect information from cameras (suchas, e.g., a surveillance camera system) and stations (STAs). A receivedimage may be processed to classify materials and objects in theenvironment. The surveillance camera system may track objects anddetermine that a moving object is blocking the line of sight oftransmission between two stations. The CNC may extract RSSIs fromreceived signals by the receiver STA. The CNC may estimate the path lossintroduced by the moving object by measuring attenuation in RSSI. Uponlearning the path loss associated to a particular object in theenvironment, the CNC may, for example, detect when the same object isabout to block the line-of-sight (LOS) between a STA and an AP (usingthe camera system) and associate the STA to a different AP to avoid theLOS blockage.

The long-term subsystem 130 (FIGS. 1A-1B, already discussed) may includea neural network (NN) f_(θ) with parameters θ (NN weights obtained aftertraining the NN) to implement the long-term memory learning aspects.FIGS. 4A-4B provide diagrams illustrating aspects of an example oftraining and inference phases for a neural network in a wireless networkcontrol system according to one or more embodiments, with reference tocomponents and features described herein including but not limited tothe figures and associated description. Training may be carried out viaself-supervised learning. As shown in FIG. 4A, a wireless networktraining environment 401 may include one or more RGB-D (imaging plusdepth sensor) cameras 410, a pair of stations 421, 422, and one or moremoving objects 430 that may block a line of sight between stations 421and 422. During the training phase, RGB-D cameras 410 may detect andtrack stations 421, 422 and moving objects 430 in the environment andmay obtain, for each tracked element, its unique identifier “i” and its3D position in the space x_(i,t) ∈

³ with respect to a known fixed inertial frame and bounding box b_(i,t)∈

³. The sub-index i references the unique identifier for the object orSTA, and the sub-index t references that the data was timestamped attime t. The inertial frame may be obtained in a previous cameracalibration phase that uses well known extrinsic camera calibrationapproaches to determine the position of all the cameras in theenvironment with respect to a common reference frame that may bedetermined at calibration time.

A central network controller (CNC, not shown in the figure), which mayincorporate the long-term subsystem 130, may collect channel stateinformation, such as RSSIs for each pair of network stations (STAs421,422) and access points (APs 441, 442), ψ∈

^(N×N−1) where N corresponds to the number of managed devices in thecommunications network. The CNC may also collect configurationparameters from each of the communications network devices (stations andaccess points) c_(t) ∈

^(N×M) where M is the number of configuration parameters that thecommunications network devices can use. The gathered information may beorganized into tuples (x_(i), b_(i), c, ψ)_(t) that are used to train afully connected neural network (e.g., long-term subsystem 130) thatlearns a mapping from tracked positions of the detected known objects tothe expected RSSI of all the network devices, conditioned by theconfiguration parameters:

f _(θ)(i,x,b,c)→{circumflex over (ψ)}.

By tracking positions of stations 421, 422 and moving objects 430,camera 410 may identify when one of the moving objects is blocking aline-of-sight (LOS) between stations 421 and 422 (e.g., STA1 and STA2 asillustrated in FIG. 4A). When the LOS between the stations 421 and 422is blocked by one of the moving objects 430, the path loss produceddepends on the material of the occluding object, the networkconfiguration and the position of the object. Attenuation introduced bya moving object 430 may be computed at the CNC using RSSI readingscollected by the stations. The neural network may then be trained basedon the data set formed by moving object/image-associated attenuation.

Once the neural network of long-term subsystem 130 has been trained,path loss from an object may be estimated by providing the object'sposition, unique identifier, bounding box, and network configurationparameters to the neural network. As shown in FIG. 4B, a wirelessnetwork operating environment 402 may be the same or similar to trainingenvironment 401, and may include one or more RGB-D (imaging plus depthsensor) cameras 410, multiple stations (such as, e.g., station 422), oneor more moving objects 430, and multiple access points (such as, e.g.,access points 441, 442). In the scenario depicted in FIG. 4B, station422 (STA2, as illustrated in FIG. 4B) may be associated with (e.g.,connected wirelessly to) access point 441 (AP1, as illustrated in thefigure). A camera 410 may track the position of stations and movingobject 430, and may predict that a moving object 430 will block theline-of-sight between access point 441 and station 422. Using the neuralnetwork of long-term subsystem 130, the attenuation of the signalbetween access point 441 and station 422 introduced by the moving object430 may be predicted. The input may be defined by the moving objectimage from camera 410. Based on the predicted attenuation, long-termsubsystem 130 may determine appropriate network configuration parametersto adjust to account for the predicted attenuation. For example, thelong-term subsystem 130 may determine that the station 422 should beswitched over and connected to access point 442 (AP2, as illustrated)where the LOS between the devices is not blocked by moving object 430.As another example, the long-term subsystem 130 may determine that thetransmitting power for the station 422 should be increased to overcomethe attenuation caused by the moving object 430.

During inference (i.e., during normal runtime operation), it may happenthat multiple stations and multiple objects are tracked. The neuralnetwork in the long-term subsystem 130 that is trained to predict RSSIvalues considers only the effects by a single object, and therefore thesystem does not model combined effects that multiple objects can have onone another. This approximation improves the computational complexityand provides a boundary to the problem complexity enabling scalabilityto many objects. To consider all of the objects to obtain theapproximated RSSI matrix {circumflex over (ψ)}, the neural network maybe used for each tracked object and a set of K predictions may beobtained. The resulting prediction may be computed as the element-wiseminimum of the obtained RSSI matrix set. This operation can be expressedas follows:

${\Psi \in {\mathbb{R}}^{K \times N \times M}},{\Psi_{ijk} \in {\mathbb{R}}},{\Psi_{i} = {{f_{\theta}\left( {i,x_{i},b_{i},c} \right)}{\forall{i \in \left\{ {0,{K - 1}} \right\}}}}},{{\hat{\psi}}_{jk} = {\min\limits_{i}\Psi_{ijk}}}$

To obtain the set of K predictions, the neural network may be runmultiple times and the minimum RSSI value computed for each pair ofnetwork devices. In embodiments, multiple runs may be performed inparallel without affecting the computation time. Accordingly, the neuralnetwork may act as a memory stored in the long-term subsystem 130. Thelong-term subsystem 130 may use the neural network to search for thebest network configuration to optimize communications quality under theobserved operating state. RSSIs collected in a station associated toother station transmissions may be used to determine optimal stationtransmission parameters to minimize interference from other devices(e.g., as may be determined by the short-term subsystem 120).

In one or more alternative embodiments, mobile stations may collectRSSIs originating from static access points (with known locations) tobuild and store RSSI maps of the environment. For example, stationsassociated with mobile automated guided vehicles would be aware of theirlocations within the environment. Location information may be sharedwith the access points where RSSI maps may be generated and stored inreal time. In one example, the RSSI maps may be stored in the short-termsubsystem 120. Access points may relay these RSSI maps to the long-termsubsystem 130, which may be incorporated in a central network controller(CNC). There, maps may be associated with the time of the day, day ofthe year, temperature and humidity levels in the atmosphere (via datacaptured by other sensors in the network) and the location of movingobjects and other assets (as classified and tracked by a surveillancecamera system).

The long-term subsystem 130 may generate and store generic path lossmodels that may be used for applications such as indoor positioning andoptimal network coverage for high reliable communications. The long-termsubsystem 130 may also record one or more sequences of events that causesignificant drops in recorded RSSI, so as to act upon an occurrence ofsuch events in the future. This stored information may be vital topredicting RSSI behavior and avoid loss in Quality of Service (QoS).Upon significant changes in the environment, such as, e.g., a layoutchange in a factory, the generated generic path loss models andlearnings may be disregarded, and new generic path loss models generatedbased on the new environment.

FIG. 5 is a diagram illustrating an example of application of RSSI mapsfor a wireless network control system in an industrial environment 501according to one or more embodiments, with reference to components andfeatures described herein including but not limited to the figures andassociated description. The industrial environment 501 may includemultiple stations, including mobile stations 511, 512 (illustrated asSTA1 and STA2 in FIG. 5 ), access points 521-523 (illustrated as AP1though AP3 in FIG. 5 ), and workers performing various tasks in theenvironment. The mobile stations 511 and 512 may collect RSSIs receivedby the access points and send the RSSI information to long-termsubsystem 130, which may be incorporated within a central networkcontroller (CNC, not shown) for the network. At a particular point intime, the mobile station 511 (STA1) may record a low RSSI from accesspoint 522 (AP2). The CNC may detect (via a camera surveillance system,not shown) the presence of a worker 530 blocking the LOS between mobilestation 511 and access point 522, and may, via the long-term subsystem130, associate the low RSSI reading to this occurrence. Based onaccumulated learnings in the long-term subsystem 130 for these types ofevents, the CNC may react by automatically increasing the transmissionpower of access point 522 to increase the RSSI and preserve the qualityof service (QoS) in the network. Alternatively, the CNC may determinethat the LOS between mobile station 511 and access point 521 (AP1) isnot blocked and react by automatically switching station 511 to connectto access point 521 to preserve network QoS.

In one or more embodiments, other channel state information may becollected and utilized by the long-term subsystem 130 in addition to, orin place of, RSSI data from device transmissions. Such channel stateinformation may be added to the RSSI maps, already discussed, or may beused to generate channel state maps, for use in performing devicepositioning within the network or predicting network QoS. Channel stateinformation may be used, for example, to estimate frequency selectivityof the wireless channel and delay spread. This information may be usedto determine if the link offers the required QoS. A low QoS may triggera change in the STA-AP association. Channel state information may alsobe used for enabling more sophisticated radio frequency (RF)fingerprinting indoor positioning algorithms.

FIGS. 6A-6B provide flowcharts illustrating a process 600 for operationof an example of a wireless network control system according to one ormore embodiments, with reference to components and features describedherein including but not limited to the figures and associateddescription. Process 600 may be implemented in wireless network controlsystem 100 described herein with reference to FIGS. 1A-1B, alreadydiscussed. More particularly, process 600 may be implemented in one ormore modules as a set of logic instructions stored in a machine- orcomputer-readable storage medium such as random access memory (RAM),read only memory (ROM), programmable ROM (PROM), firmware, flash memory,etc., in configurable logic such as, for example, programmable logicarrays (PLAs), field programmable gate arrays (FPGAs), complexprogrammable logic devices (CPLDs), in fixed-functionality logichardware using circuit technology such as, for example, applicationspecific integrated circuit (ASIC), complementary metal oxidesemiconductor (CMOS) or transistor-transistor logic (TTL) technology, orany combination thereof.

For example, computer program code to carry out operations shown inprocess 600 may be written in any combination of one or more programminglanguages, including an object oriented programming language such asJAVA, SMALLTALK, C++ or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. Additionally, logic instructions might include assemblerinstructions, instruction set architecture (ISA) instructions, machineinstructions, machine dependent instructions, microcode, state-settingdata, configuration data for integrated circuitry, state informationthat personalizes electronic circuitry and/or other structuralcomponents that are native to hardware (e.g., host processor, centralprocessing unit/CPU, microcontroller, etc.).

Turning to FIG. 6A, illustrated processing block 610 provides foradjusting, via a short-term subsystem, a communications parameter forone or more wireless communication devices based on data from one ormore sensors. The short-term subsystem may include a plurality of shortterm memory modules distributed among communication devices. Adjusting acommunications parameter may include, for example, increasingtransmission power to provide a desired QoS. As another example,enablement of packet duplication in the presence of detectedinterference may provide for high reliable communications throughredundancy (via operating in two frequency channels in parallel).

Illustrated processing block 615 provides for determining, via a neuralnetwork, a prediction of future performance of the wireless networkbased on a state of the network environment, wherein the state of thenetwork environment includes information from the short-term subsystemand location information about wireless communication devices and otherobjects in the environment.

Illustrated processing block 620 provides for determining a change innetwork configuration to improve a quality of communications in thewireless network based on the prediction of future performance of thewireless network.

Illustrated processing block 630 provides for adjusting one or morenetwork parameters, wherein the one or more network parameters includesone or more of an increase in transmission power by one of the wirelesscommunication devices or a change in an access point to be connected tothe one of the wireless communication devices.

Illustrated processing block 640 provides for tracking a plurality ofthe wireless communication devices and a plurality of the other objects,wherein the prediction of future performance of the wireless networkincludes a prediction of future attenuation in RSSI associated with atracked moving object relative to the access point and a tracked one ofthe wireless communication devices.

Turning to FIG. 6B, illustrated processing block 660 provides forreceiving a plurality of time-stamped RSSI maps, wherein each of theplurality of time-stamped RSSI maps is associated with an objectlocation, a time of day, a day of year, and one or more of temperatureor humidity levels within the network environment. RSSI maps may bebased on RSSI data originating from static access points (with knownlocations) within the network the environment. Illustrated processingblock 665 provides for generating a plurality of generic path lossmodels based on the plurality of time-stamped RSSI maps. Illustratedprocessing block 670 provides for recording a sequence of events thatcause a significant drop in RSSI. Illustrated processing block 675provides for automatically increasing the transmission power of anaccess point or automatically switching a station to connect to adifferent access point.

Illustrated processing block 680 provides for receiving additionalchannel state information. Channel state information may be used toestimate frequency selectivity of the wireless channel and delay spread.This information could be used to determine if the link offers therequired QoS. A low QoS may trigger a change in the STA-AP association.Channel state information may also be used for enabling moresophisticated RF fingerprinting indoor positioning algorithms.Illustrated processing block 685 provides for determining, via theneural network, one or more of a positioning of one or more of theobjects in the network environment or a prediction of future quality ofservice (QoS) for the wireless network.

FIG. 7 shows a block diagram illustrating an example computing system 10for adapting a network configuration based on the local networkenvironment according to one or more embodiments, with reference tocomponents and features described herein including but not limited tothe figures and associated description. The system 10 may generally bepart of an electronic device/platform having computing and/orcommunications functionality (e.g., server, cloud infrastructurecontroller, database controller, notebook computer, desktop computer,personal digital assistant/PDA, tablet computer, convertible tablet,smart phone, etc.), imaging functionality (e.g., camera, camcorder),media playing functionality (e.g., smart television/TV), wearablefunctionality (e.g., watch, eyewear, headwear, footwear, jewelry),vehicular functionality (e.g., car, truck, motorcycle), roboticfunctionality (e.g., autonomous robot), Internet of Things (IoT)functionality, etc., or any combination thereof. In the illustratedexample, the system 10 may include a host processor 12 (e.g., centralprocessing unit/CPU) having an integrated memory controller (IMC) 14that may be coupled to system memory 20. The host processor 12 mayinclude any type of processing device, such as, e.g., microcontroller,microprocessor, RISC processor, ASIC, etc., along with associatedprocessing modules or circuitry. System memory 20 may include anynon-transitory machine- or computer-readable storage medium such as RAM,ROM, PROM, EEPROM, firmware, flash memory, etc., configurable logic suchas, for example, PLAs, FPGAs, CPLDs, fixed-functionality hardware logicusing circuit technology such as, for example, ASIC, CMOS or TTLtechnology, or any combination thereof suitable for storing instructions28.

The system 10 may also include an input/output (I/O) subsystem 16. TheI/O subsystem 16 may communicate with for example, one or moreinput/output (I/O) devices 17, a network controller 24 (e.g., wiredand/or wireless NIC), and storage 22. Storage 22 may be comprised of anyappropriate non-transitory machine- or computer-readable memory type(e.g., flash memory, DRAM, SRAM (static random access memory), solidstate drive (SSD), hard disk drive (HDD), optical disk, etc.). Storage22 may include mass storage. In some embodiments, the host processor 12and/or the I/O subsystem 16 may communicate with storage 22 (all orportions thereof) via a network controller 24. In some embodiments, thesystem 10 may also include a graphics processor 26 (e.g., graphicsprocessing unit/GPU) and an AI accelerator 27. In some embodiments, thesystem 10 may also include a perception subsystem 18 (e.g., includingone or more sensors and/or cameras) and/or an actuation subsystem 19. Inan embodiment, system 10 may also include a vision processing unit(VPU), not shown.

The host processor 12 and the I/O subsystem 16 may be implementedtogether on a semiconductor die as a system on chip (SoC) 11, shownencased in a solid line. The SoC 11 may therefore operate as a computingapparatus for automated network control. In some embodiments, The SoC 11may also include one or more of the system memory 20, the networkcontroller 24, the graphics processor 26 and/or the AI accelerator 27(shown encased in dotted lines). In some embodiments, the SoC 11 mayalso include other components of system 10.

The host processor 12, the I/O subsystem 16, the graphics processor 26,the AI accelerator 27 and/or the VPU may execute program instructions 28retrieved from the system memory 20 and/or the storage 22 to perform oneor more aspects of process 600 as described herein with reference toFIGS. 6A-6B. Thus, execution of the instructions 28 may cause the SoC 11to adjust, via a short-term subsystem, a communications parameter forone or more wireless communication devices based on data from one ormore sensors, determine, via a neural network, a prediction of futureperformance of the wireless network based on a state of the networkenvironment, wherein the state of the network environment includesinformation from the short-term subsystem and location information aboutwireless communication devices and other objects in the environment, anddetermine, via the neural network, a change in network configuration toimprove a quality of communications in the wireless network based on theprediction of future performance of the wireless network. The system 10may implement one or more aspects of the wireless network control system100, the short-term subsystem 120, the long-term subsystem 130, and/orthe output response module 140 as described herein with reference toFIGS. 1A-1B, 2, 3, 4A-4B and 5 . The system 10 is therefore consideredto be performance-enhanced at least to the extent that network qualitymay be improved by adapting a network configuration based on the localnetwork environment.

Computer program code to carry out the processes described above may bewritten in any combination of one or more programming languages,including an object-oriented programming language such as JAVA,JAVASCRIPT, PYTHON, SMALLTALK, C++ or the like and/or conventionalprocedural programming languages, such as the “C” programming languageor similar programming languages, and implemented as the programinstructions 28. Additionally, the program instructions 28 may includeassembler instructions, instruction set architecture (ISA) instructions,machine instructions, machine dependent instructions, microcode,state-setting data, configuration data for integrated circuitry, stateinformation that personalizes electronic circuitry and/or otherstructural components that are native to hardware (e.g., host processor,central processing unit/CPU, microcontroller, microprocessor, etc.).

The I/O devices 17 may include one or more of input devices, such as atouch-screen, keyboard, mouse, cursor-control device, touch-screen,microphone, digital camera, video recorder, camcorder, biometricscanners and/or sensors; input devices may be used to enter informationand interact with the system 10 and/or with other devices. The I/Odevices 17 may also include one or more of output devices, such as adisplay (e.g., touch screen, liquid crystal display/LCD, light emittingdiode/LED display, plasma panels, etc.), speakers and/or other visual oraudio output devices. Input and/or output devices may be used, e.g., toprovide a user interface.

FIG. 8 shows a block diagram illustrating an example semiconductorapparatus 30 for adapting a network configuration based on the localnetwork environment according to one or more embodiments, with referenceto components and features described herein including but not limited tothe figures and associated description. The semiconductor apparatus 30may be implemented, e.g., as a chip, die, or other semiconductorpackage. The semiconductor apparatus 30 may include one or moresubstrates 32 comprised of, e.g., silicon, sapphire, gallium arsenide,etc. The semiconductor apparatus 30 may also include logic 34 comprisedof, e.g., transistor array(s) and other integrated circuit (IC)components) coupled to the substrate(s) 32. The logic 34 may beimplemented at least partly in configurable logic or fixed-functionalitylogic hardware. The logic 34 may implement system on chip (SoC) 11described above with reference to FIG. 7 . The logic 34 may implementone or more aspects of the processes described above, including process600 as described herein with reference to FIGS. 6A-6B to adjust, via ashort-term subsystem, a communications parameter for one or morewireless communication devices based on data from one or more sensors,determine, via a neural network, a prediction of future performance ofthe wireless network based on a state of the network environment,wherein the state of the network environment includes information fromthe short-term subsystem and location information about wirelesscommunication devices and other objects in the environment, anddetermine, via the neural network, a change in network configuration toimprove a quality of communications in the wireless network based on theprediction of future performance of the wireless network as describedherein with reference to FIGS. 6A-6B. The logic 34 may implement one ormore aspects of the wireless network control system 100, the short-termsubsystem 120, the long-term subsystem 130, and/or the output responsemodule 140 as described herein with reference to FIGS. 1A-1B, 2, 3,4A-4B and 5. The apparatus 30 is therefore considered to beperformance-enhanced at least to the extent that network quality may beimproved by adapting a network configuration based on the local networkenvironment.

The semiconductor apparatus 30 may be constructed using any appropriatesemiconductor manufacturing processes or techniques. For example, thelogic 34 may include transistor channel regions that are positioned(e.g., embedded) within substrate(s) 32. Thus, the interface between thelogic 34 and the substrate(s) 32 may not be an abrupt junction. Thelogic 34 may also be considered to include an epitaxial layer that isgrown on an initial wafer of the substrate(s) 34.

FIG. 9 is a block diagram illustrating an example processor core 40according to one or more embodiments, with reference to components andfeatures described herein including but not limited to the figures andassociated description. The processor core 40 may be the core for anytype of processor, such as a micro-processor, an embedded processor, adigital signal processor (DSP), a network processor, or other device toexecute code. Although only one processor core 40 is illustrated in FIG.9 , a processing element may alternatively include more than one of theprocessor core 40 illustrated in FIG. 9 . The processor core 40 may be asingle-threaded core or, for at least one embodiment, the processor core40 may be multithreaded in that it may include more than one hardwarethread context (or “logical processor”) per core.

FIG. 9 also illustrates a memory 41 coupled to processor core 40. Thememory 41 may be any of a wide variety of memories (including variouslayers of memory hierarchy) as are known or otherwise available to thoseof skill in the art. The memory 41 may include one or more code 42instruction(s) to be executed by processor core 40. The code 42 mayimplement one or more aspects of the process 600 described above. Theprocessor core 40 may implement one or more aspects of the wirelessnetwork control system 100, the short-term subsystem 120, the long-termsubsystem 130, and/or the output response module 140 as described hereinwith reference to FIGS. 1A-1B, 2, 3, 4A-4B and 5 . The processor core 40follows a program sequence of instructions indicated by the code 42.Each instruction may enter a front end portion 43 and be processed byone or more decoders 44. The decoder 44 may generate as its output amicro operation such as a fixed width micro operation in a predefinedformat, or may generate other instructions, microinstructions, orcontrol signals which reflect the original code instruction. Theillustrated front end portion 43 also includes register renaming logic46 and scheduling logic 48, which generally allocate resources and queuethe operation corresponding to the convert instruction for execution.

The processor core 40 is shown including execution logic 50 having a setof execution units 55-1 through 55-N. Some embodiments may include anumber of execution units dedicated to specific functions or sets offunctions. Other embodiments may include only one execution unit or oneexecution unit that can perform a particular function. The illustratedexecution logic 50 performs the operations specified by codeinstructions.

After completion of execution of the operations specified by the codeinstructions, back end logic 58 retires the instructions of code 42. Inone embodiment, the processor core 40 allows out of order execution butrequires in order retirement of instructions. Retirement logic 59 maytake a variety of forms as known to those of skill in the art (e.g.,re-order buffers or the like). In this manner, the processor core 40 istransformed during execution of the code 42, at least in terms of theoutput generated by the decoder, the hardware registers and tablesutilized by the register renaming logic 46, and any registers (notshown) modified by the execution logic 50.

Although not illustrated in FIG. 9 , a processing element may includeother elements on chip with the processor core 40. For example, aprocessing element may include memory control logic along with theprocessor core 40. The processing element may include I/O control logicand/or may include I/O control logic integrated with memory controllogic. The processing element may also include one or more caches.

FIG. 10 is a block diagram illustrating an example of a multi-processorbased computing system 60 according to one or more embodiments, withreference to components and features described herein including but notlimited to the figures and associated description. The multiprocessorsystem 60 includes a first processing element 70 and a second processingelement 80. While two processing elements 70 and 80 are shown, it is tobe understood that an embodiment of the system 60 may also include onlyone such processing element.

The system 60 is illustrated as a point-to-point interconnect system,wherein the first processing element 70 and the second processingelement 80 are coupled via a point-to-point interconnect 71. It shouldbe understood that any or all of the interconnects illustrated in FIG.10 may be implemented as a multi-drop bus rather than point-to-pointinterconnect.

As shown in FIG. 10 , each of the processing elements 70 and 80 may bemulticore processors, including first and second processor cores (i.e.,processor cores 74 a and 74 b and processor cores 84 a and 84 b). Suchcores 74 a, 74 b, 84 a, 84 b may be configured to execute instructioncode in a manner similar to that discussed above in connection with FIG.9 .

Each processing element 70, 80 may include at least one shared cache 99a, 99 b. The shared cache 99 a, 99 b may store data (e.g., instructions)that are utilized by one or more components of the processor, such asthe cores 74 a, 74 b and 84 a, 84 b, respectively. For example, theshared cache 99 a, 99 b may locally cache data stored in a memory 62, 63for faster access by components of the processor. In one or moreembodiments, the shared cache 99 a, 99 b may include one or moremid-level caches, such as level 2 (L2), level 3 (L3), level 4 (L4), orother levels of cache, a last level cache (LLC), and/or combinationsthereof.

While shown with only two processing elements 70, 80, it is to beunderstood that the scope of the embodiments are not so limited. Inother embodiments, one or more additional processing elements may bepresent in a given processor. Alternatively, one or more of theprocessing elements 70, 80 may be an element other than a processor,such as an accelerator or a field programmable gate array. For example,additional processing element(s) may include additional processors(s)that are the same as a first processor 70, additional processor(s) thatare heterogeneous or asymmetric to processor a first processor 70,accelerators (such as, e.g., graphics accelerators or digital signalprocessing (DSP) units), field programmable gate arrays, or any otherprocessing element. There can be a variety of differences between theprocessing elements 70, 80 in terms of a spectrum of metrics of meritincluding architectural, micro architectural, thermal, power consumptioncharacteristics, and the like. These differences may effectivelymanifest themselves as asymmetry and heterogeneity amongst theprocessing elements 70, 80. For at least one embodiment, the variousprocessing elements 70, 80 may reside in the same die package.

The first processing element 70 may further include memory controllerlogic (MC) 72 and point-to-point (P-P) interfaces 76 and 78. Similarly,the second processing element 80 may include a MC 82 and P-P interfaces86 and 88. As shown in FIG. 10 , MC's 72 and 82 couple the processors torespective memories, namely a memory 62 and a memory 63, which may beportions of main memory locally attached to the respective processors.While the MC 72 and 82 is illustrated as integrated into the processingelements 70, 80, for alternative embodiments the MC logic may bediscrete logic outside the processing elements 70, 80 rather thanintegrated therein.

The first processing element 70 and the second processing element 80 maybe coupled to an I/O subsystem 90 via P-P interconnects 76 and 86,respectively. As shown in FIG. 10 , the I/O subsystem 90 includes P-Pinterfaces 94 and 98. Furthermore, the I/O subsystem 90 includes aninterface 92 to couple I/O subsystem 90 with a high performance graphicsengine 64. In one embodiment, a bus 73 may be used to couple thegraphics engine 64 to the I/O subsystem 90. Alternately, apoint-to-point interconnect may couple these components.

In turn, the I/O subsystem 90 may be coupled to a first bus 65 via aninterface 96. In one embodiment, the first bus 65 may be a PeripheralComponent Interconnect (PCI) bus, or a bus such as a PCI Express bus oranother third generation I/O interconnect bus, although the scope of theembodiments are not so limited.

As shown in FIG. 10 , various I/O devices 65 a (e.g., biometricscanners, speakers, cameras, sensors) may be coupled to the first bus65, along with a bus bridge 66 which may couple the first bus 65 to asecond bus 67. In one embodiment, the second bus 67 may be a low pincount (LPC) bus. Various devices may be coupled to the second bus 67including, for example, a keyboard/mouse 67 a, communication device(s)67 b, and a data storage unit 68 such as a disk drive or other massstorage device which may include code 69, in one embodiment. Theillustrated code 69 may implement one or more aspects of the processesdescribed above, including process 600 as described herein withreference to FIGS. 6A-6B. The illustrated code 69 may be similar to code42 (FIG. 9 ), already discussed. Further, an audio I/O 67 c may becoupled to the second bus 67 and a battery 61 may supply power to thecomputing system 60. The system 60 may implement one or more aspects ofthe wireless network control system 100, the short-term subsystem 120,the long-term subsystem 130, and/or the output response module 140 asdescribed herein with reference to FIGS. 1A-1B, 2, 3, 4A-4B and 5 .

Note that other embodiments are contemplated. For example, instead ofthe point-to-point architecture of FIG. 10 , a system may implement amulti-drop bus or another such communication topology. Also, theelements of FIG. 10 may alternatively be partitioned using more or fewerintegrated chips than shown in FIG. 10 .

Embodiments of each of the above systems, devices, components and/ormethods, including the system 10, the semiconductor apparatus 30, theprocessor core 40, the system 60, the wireless network control system100, the short-term subsystem 120, the long-term subsystem 130, theoutput response module 140, process 600, and/or any other systemcomponents, may be implemented in hardware, software, or any suitablecombination thereof. For example, hardware implementations may includeconfigurable logic such as, for example, programmable logic arrays(PLAs), field programmable gate arrays (FPGAs), complex programmablelogic devices (CPLDs), or fixed-functionality logic hardware usingcircuit technology such as, for example, application specific integratedcircuit (ASIC), complementary metal oxide semiconductor (CMOS) ortransistor-transistor logic (TTL) technology, or any combinationthereof.

Alternatively, or additionally, all or portions of the foregoing systemsand/or components and/or methods may be implemented in one or moremodules as a set of logic instructions stored in a machine- orcomputer-readable storage medium such as random access memory (RAM),read only memory (ROM), programmable ROM (PROM), firmware, flash memory,etc., to be executed by a processor or computing device. For example,computer program code to carry out the operations of the components maybe written in any combination of one or more operating system (OS)applicable/appropriate programming languages, including anobject-oriented programming language such as PYTHON, PERL, JAVA,SMALLTALK, C++, C # or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages.

ADDITIONAL NOTES AND EXAMPLES

Example 1 includes a network control system, comprising a sensorinterface to obtain data from a plurality of sensors, a first subsystemcomprising a plurality of distributed short-term memory modules, eachshort-term memory module associated with a wireless communicationdevice, the first subsystem to adjust a communications parameter for oneor more of the wireless communication devices based on data from one ormore of the plurality of sensors, and a second subsystem coupled to thesensor interface and to the first subsystem comprising a processor, theprocessor including one or more substrates and logic coupled to the oneor more substrates, wherein the logic is implemented at least partly inone or more of configurable logic or fixed-functionality hardware logic,the logic coupled to the one or more substrates to determine, via aneural network, a prediction of future performance of a wireless networkbased on a state of a network environment, wherein the state of thenetwork environment includes information from the first subsystem andlocation information about the wireless communication devices and otherobjects in the network environment, and determine a change in networkconfiguration to improve a quality of communications in the wirelessnetwork based on the prediction of future performance of the wirelessnetwork.

Example 2 includes the network control system of Example 1, wherein todetermine a change in network configuration to improve a quality ofcommunications in the wireless network the logic coupled to the one ormore substrates is to adjust one or more network parameters, and whereinthe one or more network parameters includes one or more of an increasein transmission power by one of the wireless communication devices or achange in an access point to be connected to the one of the wirelesscommunication devices.

Example 3 includes the network control system of Example 1, wherein thefirst subsystem comprises a short-term subsystem and the secondsubsystem comprises a long-term subsystem, and wherein the informationfrom the first subsystem is to include one or more received signalstrength indication (RSSI) maps, each RSSI map associated with a channelstate between an access point and one of the wireless communicationdevices, and wherein the prediction of future performance of thewireless network comprises a prediction of future RSSI values based ondetermined relationships between the RSSI maps and object locationsrelative to the access point and the wireless communication devices.

Example 4 includes the network control system of Example 3, wherein thesystem is to track a plurality of the wireless communication devices anda plurality of the other objects, and wherein the prediction of futureperformance of the wireless network includes a prediction of futureattenuation in RSSI associated with a tracked moving object relative tothe access point and a tracked one of the wireless communicationdevices.

Example 5 includes the network control system of Example 3, wherein thelogic coupled to the one or more substrates is further to receive aplurality of time-stamped RSSI maps, wherein each of the plurality oftime-stamped RSSI maps is associated with an object location, a time ofday, a day of year, and one or more of temperature or humidity levelswithin the network environment, generate a plurality of generic pathloss models based on the plurality of time-stamped RSSI maps, and recorda sequence of events that cause a significant drop in RSSI, wherein todetermine a change in network configuration is based on the plurality ofgeneric path loss models and the sequence of events.

Example 6 includes the network control system of any of Examples 1-5,wherein the logic coupled to the one or more substrates is further toreceive additional channel state information, and determine one or moreof a positioning of one or more of the objects in the networkenvironment or a prediction of future quality of service (QoS) for thewireless network.

Example 7 includes a semiconductor apparatus comprising one or moresubstrates, and logic coupled to the one or more substrates, wherein thelogic is implemented at least partly in one or more of configurablelogic or fixed-functionality hardware logic, the logic coupled to theone or more substrates to adjust, via a first subsystem, acommunications parameter for one or more wireless communication devicesbased on data from one or more of a plurality of sensors, determine, viaa neural network, a prediction of future performance of a wirelessnetwork based on a state of a network environment, wherein the state ofthe network environment includes information from the first subsystemand location information about the one or more wireless communicationdevices and other objects in the network environment, and determine achange in network configuration to improve a quality of communicationsin the wireless network based on the prediction of future performance ofthe wireless network.

Example 8 includes the semiconductor apparatus of Example 7, wherein todetermine a change in network configuration to improve a quality ofcommunications in the wireless network the logic coupled to the one ormore substrates is to adjust one or more network parameters, and whereinthe one or more network parameters includes one or more of an increasein transmission power by one of the wireless communication devices or achange in an access point to be connected to the one of the wirelesscommunication devices.

Example 9 includes the semiconductor apparatus of Example 7, wherein thefirst subsystem comprises a short-term subsystem, and wherein theinformation from the first subsystem is to include one or more receivedsignal strength indication (RSSI) maps, each RSSI map associated with achannel state between an access point and one of the wirelesscommunication devices, and wherein the prediction of future performanceof the wireless network comprises a prediction of future RSSI valuesbased on determined relationships between the RSSI maps and objectlocations relative to the access point and the wireless communicationdevices.

Example 10 includes the semiconductor apparatus of Example 9, whereinthe logic coupled to the one or more substrates is to track a pluralityof the wireless communication devices and a plurality of the otherobjects, and wherein the prediction of future performance of thewireless network includes a prediction of future attenuation in RSSIassociated with a tracked moving object relative to the access point anda tracked one of the wireless communication devices.

Example 11 includes the semiconductor apparatus of Example 9, whereinthe logic coupled to the one or more substrates is further to receive aplurality of time-stamped RSSI maps, wherein each of the plurality oftime-stamped RSSI maps is associated with an object location, a time ofday, a day of year, and one or more of temperature or humidity levelswithin the network environment, generate a plurality of generic pathloss models based on the plurality of time-stamped RSSI maps, and recorda sequence of events that cause a significant drop in RSSI, wherein todetermine a change in network configuration is based on the plurality ofgeneric path loss models and the sequence of events.

Example 12 includes the semiconductor apparatus of any of Examples 7-11,wherein the logic coupled to the one or more substrates is further toreceive additional channel state information, and determine one or moreof a positioning of one or more of the objects in the networkenvironment or a prediction of future quality of service (QoS) for thewireless network.

Example 13 includes the semiconductor apparatus of Examples 7, whereinthe logic coupled to the one or more substrates includes transistorchannel regions that are positioned within the one or more substrates.

Example 14 includes at least one non-transitory computer readablestorage medium comprising a set of instructions which, when executed bya computing system, cause the computing system to adjust, via a firstsubsystem, a communications parameter for one or more wirelesscommunication devices based on data from one or more of a plurality ofsensors, determine, via a neural network, a prediction of futureperformance of a wireless network based on a state of a networkenvironment, wherein the state of the network environment includesinformation from the first subsystem and location information about theone or more wireless communication devices and other objects in thenetwork environment, and determine a change in network configuration toimprove a quality of communications in the wireless network based on theprediction of future performance of the wireless network.

Example 15 includes the at least one non-transitory computer readablestorage medium of Example 14, wherein to determine a change in networkconfiguration to improve a quality of communications in the wirelessnetwork, the instructions, when executed, cause the computing system toadjust one or more network parameters, and wherein the one or morenetwork parameters includes one or more of an increase in transmissionpower by one of the wireless communication devices or a change in anaccess point to be connected to the one of the wireless communicationdevices.

Example 16 includes the at least one non-transitory computer readablestorage medium of Example 14, wherein the first subsystem comprises ashort-term subsystem, and wherein the information from the firstsubsystem is to include one or more received signal strength indication(RSSI) maps, each RSSI map associated with a channel state between anaccess point and one of the wireless communication devices, and whereinthe prediction of future performance of the wireless network comprises aprediction of future RSSI values based on determined relationshipsbetween the RSSI maps and object locations relative to the access pointand the wireless communication devices.

Example 17 includes the at least one non-transitory computer readablestorage medium of Example 16, wherein the instructions, when executed,further cause the computing system to track a plurality of the wirelesscommunication devices and a plurality of the other objects, and whereinthe prediction of future performance of the wireless network includes aprediction of future attenuation in RSSI associated with a trackedmoving object relative to the access point and a tracked one of thewireless communication devices.

Example 18 includes the at least one non-transitory computer readablestorage medium of Example 16, wherein the instructions, when executed,further cause the computing system to receive a plurality oftime-stamped RSSI maps, wherein each of the plurality of time-stampedRSSI maps is associated with an object location, a time of day, a day ofyear, and one or more of temperature or humidity levels within thenetwork environment, generate a plurality of generic path loss modelsbased on the plurality of time-stamped RSSI maps, and record a sequenceof events that cause a significant drop in RSSI, wherein to determine achange in network configuration is based on the plurality of genericpath loss models and the sequence of events.

Example 19 includes the at least one non-transitory computer readablestorage medium of any of Examples 14-18, wherein the instructions, whenexecuted, further cause the computing system to receive additionalchannel state information, and determine one or more of a positioning ofone or more of the objects in the network environment or a prediction offuture quality of service (QoS) for the wireless network.

Example 20 includes a method of automated network control, comprisingadjusting, via a first subsystem, a communications parameter for one ormore wireless communication devices based on data from one or more of aplurality of sensors, determining, via a neural network, a prediction offuture performance of a wireless network based on a state of a networkenvironment, wherein the state of the network environment includesinformation from the first subsystem and location information about theone or more wireless communication devices and other objects in thenetwork environment, and determining a change in network configurationto improve a quality of communications in the wireless network based onthe prediction of future performance of the wireless network.

Example 21 includes the method of Example 20, wherein determining achange in network configuration to improve a quality of communicationsin the wireless network comprises adjusting one or more networkparameters, and wherein the one or more network parameters includes oneor more of an increase in transmission power by one of the wirelesscommunication devices or a change in an access point to be connected tothe one of the wireless communication devices.

Example 22 includes the method of Example 20, wherein the firstsubsystem comprises a short-term subsystem, and wherein the informationfrom the first subsystem is to include one or more received signalstrength indication (RSSI) maps, each RSSI map associated with a channelstate between an access point and one of the wireless communicationdevices, and wherein the prediction of future performance of thewireless network comprises a prediction of future RSSI values based ondetermined relationships between the RSSI maps and object locationsrelative to the access point and the wireless communication devices.

Example 23 includes the method of Example 22, further comprisingtracking a plurality of the wireless communication devices and aplurality of the other objects, wherein the prediction of futureperformance of the wireless network includes a prediction of futureattenuation in RSSI associated with a tracked moving object relative tothe access point and a tracked one of the wireless communicationdevices.

Example 24 includes the method of Example 22, further comprisingreceiving a plurality of time-stamped RSSI maps, wherein each of theplurality of time-stamped RSSI maps is associated with an objectlocation, a time of day, a day of year, and one or more of temperatureor humidity levels within the network environment, generating aplurality of generic path loss models based on the plurality oftime-stamped RSSI maps, and recording a sequence of events that cause asignificant drop in RSSI, wherein to determine a change in networkconfiguration is based on the plurality of generic path loss models andthe sequence of events.

Example 25 includes the method of any of Examples 20-24, furthercomprising receiving additional channel state information, anddetermining one or more of a positioning of one or more of the objectsin the network environment or a prediction of future quality of service(QoS) for the wireless network.

Example 26 includes an apparatus comprising means for performing themethod of any of Examples 20-24.

Thus, technology described herein enables highly-reliable wirelesscommunications in wireless communication systems for a variety ofapplications, including industrial applications. The technology alsoprovides for reliable wireless communications by actively managingnetwork devices using a short-term/long-term paradigm that includesenvironment sensing to determine the actions to be taken. Using theshort-term/long-term structure, management of a network for highreliable operation may be achieved by relying on memories andexperiences observed in the past.

Embodiments are applicable for use with all types of semiconductorintegrated circuit (“IC”) chips. Examples of these IC chips include butare not limited to processors, controllers, chipset components,programmable logic arrays (PLAs), memory chips, network chips, systemson chip (SoCs), SSD/NAND controller ASICs, and the like. In addition, insome of the drawings, signal conductor lines are represented with lines.Some may be different, to indicate more constituent signal paths, have anumber label, to indicate a number of constituent signal paths, and/orhave arrows at one or more ends, to indicate primary information flowdirection. This, however, should not be construed in a limiting manner.Rather, such added detail may be used in connection with one or moreexemplary embodiments to facilitate easier understanding of a circuit.Any represented signal lines, whether or not having additionalinformation, may actually comprise one or more signals that may travelin multiple directions and may be implemented with any suitable type ofsignal scheme, e.g., digital or analog lines implemented withdifferential pairs, optical fiber lines, and/or single-ended lines.

Example sizes/models/values/ranges may have been given, althoughembodiments are not limited to the same. As manufacturing techniques(e.g., photolithography) mature over time, it is expected that devicesof smaller size could be manufactured. In addition, well knownpower/ground connections to IC chips and other components may or may notbe shown within the figures, for simplicity of illustration anddiscussion, and so as not to obscure certain aspects of the embodiments.Further, arrangements may be shown in block diagram form in order toavoid obscuring embodiments, and also in view of the fact that specificswith respect to implementation of such block diagram arrangements arehighly dependent upon the computing system within which the embodimentis to be implemented, i.e., such specifics should be well within purviewof one skilled in the art. Where specific details (e.g., circuits) areset forth in order to describe example embodiments, it should beapparent to one skilled in the art that embodiments can be practicedwithout, or with variation of, these specific details. The descriptionis thus to be regarded as illustrative instead of limiting.

The term “coupled” may be used herein to refer to any type ofrelationship, direct or indirect, between the components in question,and may apply to electrical, mechanical, fluid, optical,electromagnetic, electromechanical or other connections. In addition,the terms “first”, “second”, etc. may be used herein only to facilitatediscussion, and carry no particular temporal or chronologicalsignificance unless otherwise indicated.

As used in this application and in the claims, a list of items joined bythe term “one or more of” may mean any combination of the listed terms.For example, the phrases “one or more of A, B or C” may mean A; B; C; Aand B; A and C; B and C; or A, B and C.

Those skilled in the art will appreciate from the foregoing descriptionthat the broad techniques of the embodiments can be implemented in avariety of forms. Therefore, while the embodiments have been describedin connection with particular examples thereof, the true scope of theembodiments should not be so limited since other modifications willbecome apparent to the skilled practitioner upon a study of thedrawings, specification, and following claims.

1. (canceled)
 2. An apparatus comprising: an interface; instructions inthe apparatus; programmable circuitry to execute the instructions to atleast: access wireless communication information from an access point,the access point in a wireless network within a first environment;analyze the wireless communication information based at least in part ona received signal strength indicator (RSSI) within the wireless network,the RSSI predicted by a neural network of the apparatus, the neuralnetwork trained using reinforcement learning; detect a condition of thewireless network based on analysis of the wireless communicationinformation; and change a setting of the access point based on thedetected condition.
 3. The apparatus of claim 2, wherein the accesspoint is a first access point and the wireless communication informationincludes information related to communications transmitted by a secondaccess point, the second access point different from the first accesspoint.
 4. The apparatus of claim 3, wherein the information related tocommunications transmitted by the second access point is sniffed by thefirst access point.
 5. The apparatus of claim 2, wherein the updatedsetting updates operation of the wireless access point instantaneously.6. The apparatus of claim 2, wherein the programmable circuitry is tocause the access point to use the updated setting.
 7. The apparatus ofclaim 2, wherein the programmable circuitry is to update the setting tochange a transmission power of the access point.
 8. The apparatus ofclaim 2, wherein the programmable circuitry is to update the setting tochange a wireless transmission channel of the access point.
 9. Theapparatus of claim 2, wherein the programmable circuitry is to detect alocation of an object using the wireless communication information. 10.At least one non-transitory computer readable storage medium comprisinginstructions to cause processor circuitry to at least: access wirelesscommunication information from an access point, the access point in awireless network within a first environment; analyze the wirelesscommunication information based at least in part on a received signalstrength indicator (RSSI) within the wireless network, the RSSIpredicted by a neural network, the neural network trained usingreinforcement learning; detect a condition of the wireless network basedon analysis of the wireless communication information; and change asetting of the access point based on the detected condition.
 11. The atleast one non-transitory computer readable storage medium of claim 10,wherein the access point is a first access point and the wirelesscommunication information includes information related to communicationstransmitted by a second access point, the second access point differentfrom the first access point.
 12. The at least one non-transitorycomputer readable storage medium of claim 11, wherein the informationrelated to communications transmitted by the second access point issniffed by the first access point.
 13. The at least one non-transitorycomputer readable storage medium of claim 10, wherein the updatedsetting updates operation of the wireless access point instantaneously.14. The at least one non-transitory computer readable storage medium ofclaim 10, wherein the instructions are to cause the processor circuitryto cause the access point to use the updated setting.
 15. The at leastone non-transitory computer readable storage medium of claim 10, whereinthe instructions cause the processor circuitry to update the setting tochange a transmission power of the access point.
 16. The at least onenon-transitory computer readable storage medium of claim 10, wherein theinstructions cause the processor circuitry to update the setting tochange a wireless transmission channel of the access point.
 17. The atleast one non-transitory computer readable storage medium of claim 10,wherein the instructions are to cause the processor circuitry to detecta location of an object using the wireless communication information.18. A method comprising: accessing wireless communication informationfrom an access point, the access point in a wireless network within afirst environment; analyzing, by executing an instruction with at leastone processor, the wireless communication information based at least inpart based on a received signal strength indicator (RSSI) within thewireless network, the RSSI predicted by a neural network, the neuralnetwork trained using reinforcement learning; detecting a condition ofthe wireless network based on analysis of the wireless communicationinformation; and change a setting of the access point based on thedetected condition.
 19. The method of claim 18, wherein the access pointis a first access point and the wireless communication informationincludes information related to communications transmitted by a secondaccess point, the second access point different from the first accesspoint.
 20. The method of claim 19, wherein the information related tocommunications transmitted by the second access point is sniffed by thefirst access point.
 21. The method of claim 18, wherein the updatedsetting updates operation of the wireless access point instantaneously.22. The method of claim 18, further including causing the access pointto use the updated setting.
 23. The method of claim 18, wherein theupdated setting includes a change to a transmission power of the accesspoint.
 24. The method of claim 18, wherein the updated setting includesa change to a wireless transmission channel of the access point.
 25. Themethod of claim 18, further including detecting a location of an objectusing the wireless communication information.